{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e820f865"
      },
      "source": [
        "# Example: Hierarchical clustering on the Zheng dataset\n",
        "\n",
        "This notebook shows a structured GraphHDBSCAN workflow on the Zheng dataset in order to reproduce condensed tree that is reported in GraphHDBSCAN* paper.\n",
        "\n",
        "We go through:\n",
        "1. package installation\n",
        "2. imports and setup\n",
        "3. data loading / preparation\n",
        "4. model construction and fitting\n",
        "5. condensed tree visualization\n",
        "6. optional interactive exploration\n",
        "\n",
        "The static condensed tree is kept in the notebook because it renders reliably in the built documentation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2a49b422"
      },
      "source": [
        "## Installation\n",
        "\n",
        "These commands are only needed when you run the notebook in a fresh environment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0b3afe08"
      },
      "source": [
        "Install required package(s):\n",
        "\n",
        "```bash\n",
        "!pip install git+https://github.com/Campello-Lab/GraphHDBSCAN.git\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0ac05a0"
      },
      "source": [
        "## Build and fit the model\n",
        "\n",
        "Configure the GraphHDBSCAN model, fit it on the dataset, and inspect the resulting clustering state."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "nzoye5UBSRAc"
      },
      "outputs": [],
      "source": [
        "from coresg_graphhdbscan import GraphCoreSGHDBSCAN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ad41838e"
      },
      "source": [
        "## Load and prepare the dataset\n",
        "\n",
        "Load the Zheng dataset and prepare the matrix or AnnData object used by the clustering pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1FwLvVWSw-P",
        "outputId": "04fbf73a-c2de-49c1-bb6a-b58875ec5048"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Available columns in adata.obs: ['label', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'n_genes', 'norm_factor']\n"
          ]
        }
      ],
      "source": [
        "import yaml\n",
        "import scanpy as sc\n",
        "from sklearn.neighbors import NearestNeighbors as NN\n",
        "from sklearn.metrics import pairwise_distances\n",
        "# Load the YAML configuration file\n",
        "with open(\"config.yaml\", \"r\") as f:\n",
        "    config = yaml.safe_load(f)\n",
        "\n",
        "Zheng_config = config[\"DATASETS\"][\"Zheng\"]\n",
        "expected_cell_label = Zheng_config[\"cell_labels\"]\n",
        "\n",
        "adata = sc.read_h5ad(\"/content/Zheng.h5ad\")\n",
        "# Print available columns in adata.obs\n",
        "available_columns = list(adata.obs.columns)\n",
        "print(\"Available columns in adata.obs:\", available_columns)\n",
        "\n",
        "cell_label_key = expected_cell_label\n",
        "\n",
        "# Extract the count matrix and cell labels\n",
        "count_matrix = adata.X  # Cells as rows and genes as columns\n",
        "true_labels = adata.obs[cell_label_key]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "1x2y_IyFTN12"
      },
      "outputs": [],
      "source": [
        "g = GraphCoreSGHDBSCAN(\n",
        "    min_samples=range(2,10),\n",
        "    sim_graph_method=\"jaccard_phenograph\",\n",
        "    n_neighbors=7,\n",
        "    no_noise=True,\n",
        "    metric=\"euclidean\",\n",
        "    min_cluster_size=60\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXK_0VcyVxlS",
        "outputId": "c83acbdd-dec8-4da3-e9a5-e0825a062cc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using neighbor information from provided graph, rather than computing neighbors directly\n",
            "Jaccard graph constructed in 0.49872446060180664 seconds\n",
            "Sorting communities by size, please wait ...\n",
            "PhenoGraph completed in 0.679931640625 seconds\n",
            "[CORE-SG] (precomputed) CORE-SG graph has 1847277 edges\n",
            "[CORE-SG] m= 2: MST+tree+labels in 1.0235s\n",
            "[CORE-SG] m= 3: MST+tree+labels in 0.8560s\n",
            "[CORE-SG] m= 4: MST+tree+labels in 0.8922s\n",
            "[CORE-SG] m= 5: MST+tree+labels in 0.8917s\n",
            "[CORE-SG] m= 6: MST+tree+labels in 0.8946s\n",
            "[CORE-SG] m= 7: MST+tree+labels in 0.8869s\n",
            "[CORE-SG] m= 8: MST+tree+labels in 0.9154s\n",
            "[CORE-SG] m= 9: MST+tree+labels in 0.8035s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GraphCoreSGHDBSCAN(min_samples_list=[2, 3, 4, 5, 6, 7, 8, 9], metric='euclidean', eps=1e-12, min_cluster_size=60, X_=None, N_=None, D_=None, core_={}, kmax_=None, edges_ut_=None, idx_with_self_=None, dst_with_self_=None, idx_no_self_=None, dst_no_self_=None, A_knn_=None, msts_={}, mst_times_={}, models_={}, times_={})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "g.fit(adata.X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94bc019a"
      },
      "source": [
        "## Visualize the hierarchy\n",
        "\n",
        "Start with a static condensed tree for reliable rendering in the documentation, then show the interactive widget for live notebook use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "46a88324",
        "outputId": "b883b217-a3f6-4e68-bfd9-0954cf3d8f13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Condensed Tree for m = 7'}, ylabel='$\\\\lambda$ value'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "g.plot_condensed_tree(7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "669498d5"
      },
      "source": [
        "> The widget below works best in a live Jupyter session. In rendered HTML documentation, widget interactivity may be limited."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427,
          "referenced_widgets": [
            "a9560360d6704f98a25966c41cd1330a",
            "7bd34992696548d1b9391d6550c9b137",
            "7cc95ab268f74f249d68456e71a79829",
            "20ff7827c8ab4805ac2217558a240990",
            "49061ebad8e4496981d755c306f66305",
            "c56df06bd55942cb9a047cf1e5fca9a8",
            "14fa7580507240f694c17040f41bc9f5"
          ]
        },
        "id": "65d6b144",
        "outputId": "60b93ef6-0bc8-4e28-f7e7-46bf0da4f404"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(SelectionSlider(continuous_update=False, description='m', layout=Layout(width='500px'), options…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9560360d6704f98a25966c41cd1330a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "SelectionSlider(continuous_update=False, description='m', layout=Layout(width='500px'), options=(2, 3, 4, 5, 6…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bd34992696548d1b9391d6550c9b137"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "g.interactive_condensed_tree()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "qxfdoRVk-9qz",
        "outputId": "0346fd5d-9b22-4176-c2d6-a621422cf204"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "ax = g.plot_condensed_tree(7)\n",
        "ax.set_ylim(1.32, 0.97)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "a9560360d6704f98a25966c41cd1330a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_7bd34992696548d1b9391d6550c9b137",
              "IPY_MODEL_7cc95ab268f74f249d68456e71a79829"
            ],
            "layout": "IPY_MODEL_20ff7827c8ab4805ac2217558a240990"
          }
        },
        "7bd34992696548d1b9391d6550c9b137": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SelectionSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SelectionSliderModel",
            "_options_labels": [
              "2",
              "3",
              "4",
              "5",
              "6",
              "7",
              "8",
              "9"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "SelectionSliderView",
            "continuous_update": false,
            "description": "m",
            "description_tooltip": null,
            "disabled": false,
            "index": 0,
            "layout": "IPY_MODEL_49061ebad8e4496981d755c306f66305",
            "orientation": "horizontal",
            "readout": true,
            "style": "IPY_MODEL_c56df06bd55942cb9a047cf1e5fca9a8"
          }
        },
        "7cc95ab268f74f249d68456e71a79829": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_14fa7580507240f694c17040f41bc9f5",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x600 with 2 Axes>",
                  "image/png": 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